Work Losses Related To Inflammatory Bowel Disease in The United States: Results From The National Health Interview Survey
Bibliographic record
Abstract
BACKGROUND: U.S. studies using varying methodologies have reported different estimates for the indirect, or nonmedical cost per person with inflammatory bowel disease (IBD). Our analysis contributes to this literature by using the 1999 sample of the National Health Interview Survey (NHIS) to estimate the work-loss effect of IBD on work in the United States and the associated cost to society. METHODS: A weighted logistic regression model was used to estimate the OR of being out of the labor force as determined by predictive variables, including having been diagnosed with IBD, with or without symptoms. Controls included health status indicators and demographic variables. For those people in the labor force, a second analysis was performed to determine the relative influence of the same variables on working less than 12 months versus the entire year. SUDAAN 8.0 was used to generate population estimates, systematically correcting for survey design. RESULTS: Of IBD patients who had experienced symptoms in the past 12 months, 31.5% reported being out of the labor force (OR = 2.14, relative to the non-IBD group). We estimated the excess in the nonparticipation rate attributable to IBD with symptoms in the past 12 months in the United States to be 12.3%. Based on this, the indirect cost of nonparticipation attributable to IBD in 1998/1999 was more than $3.6 billion U.S. dollars (USD) or $5228 USD per person with IBD and symptoms. According to the second weighted logistic regression, for those who are in the labor force, having IBD had no association with the duration of work. CONCLUSIONS: By using directly observed data in our analysis, this method of estimation can be used to predict the overall paid-employment burden of IBD.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".